Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors
Liang, Yunji1; Wang, Xin2; Yu, Zhiwen1; Guo, Bin1; Zheng, Xiaolong3; Samtani, Sagar4
Source PublicationACM TRANSACTIONS ON SENSOR NETWORKS
ISSN1550-4859
2021-08-01
Volume17Issue:3Pages:28
Corresponding AuthorLiang, Yunji(liangyunji@nwpu.edu.cn)
AbstractWith the proliferation of Internet of Things (IoT) devices in the consumer market, the unprecedented sensing capability of IoT devices makes it possible to develop advanced sensing and complex inference tasks by leveraging heterogeneous sensors embedded in IoT devices. However, the limited power supply and the restricted computation capability make it challenging to conduct seamless sensing and continuous inference tasks on resource-constrained devices. How to conduct energy-efficient sensing and perform rich-sensor inference tasks on IoT devices is crucial for the success of IoT applications. Therefore, we propose a novel energyefficient collaborative sensing framework to optimize the energy consumption of IoT devices. Specifically, we explore the latent correlations among heterogeneous sensors via an attention mechanism in temporal convolutional network to quantify the dependency among sensors, and characterize the heterogeneous sensors in terms of energy consumption to categorize them into low-power sensors and energy-intensive sensors. Finally, to decrease the sampling frequency of energy-intensive sensors, we propose a multi-task learning strategy to predict the statuses of energy-intensive sensors based on the low-power sensors. To evaluate the performance of the proposed collaborative sensing framework, we develop a mobile application to collect concurrent heterogeneous data streams from all sensors embedded in Huawei Mate 8. The experimental results show that latent correlation learning is greatly helpful to understand the latent correlations among heterogeneous streams, and it is feasible to predict the statuses of energy-intensive sensors by low-power sensors with high accuracy and fast convergence. In terms of energy consumption, the proposed collaborative sensing framework is able to preserve the energy consumption of IoT devices by nearly 50% for continuous data acquisition tasks.
KeywordEnergy efficiency latent correlation learning collaboration sensing internet of things temporal convolutional network attention mechanism multi-task learning
DOI10.1145/3448416
WOS KeywordCONTINUOUS AUTHENTICATION ; INTELLIGENCE ; INDUSTRY ; INTERNET ; DEVICES
Indexed BySCI
Language英语
Funding ProjectNational Major Program for Technological Innovation[2018AAA0100500] ; natural science foundation of China[61902320] ; fundamental research funds for the central universities[31020180QD140]
Funding OrganizationNational Major Program for Technological Innovation ; natural science foundation of China ; fundamental research funds for the central universities
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Telecommunications
WOS IDWOS:000777801400013
PublisherASSOC COMPUTING MACHINERY
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48326
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorLiang, Yunji
Affiliation1.Northwestern Polytech Univ, Sch Comp Sci, Xian 710129, Shaanxi, Peoples R China
2.Northwestern Polytech Univ, Sch Software, Xian 710129, Shaanxi, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
4.Indiana Univ, Kelley Sch Business, Bloomington, IN 47405 USA
Recommended Citation
GB/T 7714
Liang, Yunji,Wang, Xin,Yu, Zhiwen,et al. Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors[J]. ACM TRANSACTIONS ON SENSOR NETWORKS,2021,17(3):28.
APA Liang, Yunji,Wang, Xin,Yu, Zhiwen,Guo, Bin,Zheng, Xiaolong,&Samtani, Sagar.(2021).Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors.ACM TRANSACTIONS ON SENSOR NETWORKS,17(3),28.
MLA Liang, Yunji,et al."Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors".ACM TRANSACTIONS ON SENSOR NETWORKS 17.3(2021):28.
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